class_docstr: This `Attention` module performs a dot product between a vector of weights and some
  combination of the two input vectors, followed by an (optional) activation function.  The
  combination used is configurable.
  
  If the two vectors are `x` and `y`, we allow the following kinds of combinations : `x`,
  `y`, `x*y`, `x+y`, `x-y`, `x/y`, where each of those binary operations is performed
  elementwise.  You can list as many combinations as you want, comma separated.  For example, you
  might give `x,y,x*y` as the `combination` parameter to this class.  The computed similarity
  function would then be `w^T [x; y; x*y] + b`, where `w` is a vector of weights, `b` is a
  bias parameter, and `[;]` is vector concatenation.
  
  Note that if you want a bilinear similarity function with a diagonal weight matrix W, where the
  similarity function is computed as `x * w * y + b` (with `w` the diagonal of `W`), you can
  accomplish that with this class by using "x*y" for `combination`.
  
  Registered as an `Attention` with name "linear".
  
  # Parameters
  
  tensor_1_dim : `int`, required
      The dimension of the first tensor, `x`, described above.  This is `x.size()[-1]` - the
      length of the vector that will go into the similarity computation.  We need this so we can
      build weight vectors correctly.
  tensor_2_dim : `int`, required
      The dimension of the second tensor, `y`, described above.  This is `y.size()[-1]` - the
      length of the vector that will go into the similarity computation.  We need this so we can
      build weight vectors correctly.
  combination : `str`, optional (default=`"x,y"`)
      Described above.
  activation : `Activation`, optional (default=`linear`)
      An activation function applied after the `w^T * [x;y] + b` calculation.  Default is
      linear, i.e. no activation.
  normalize : `bool`, optional (default=`True`)
code: def reset_parameters(self):
      std = math.sqrt(6 / (self._weight_vector.size(0) + 1))
      self._weight_vector.data.uniform_(-std, std)
      self._bias.data.fill_(0)
  
file_path: allennlp/allennlp/modules/attention/linear_attention.py
imports: import math
  import torch
  from torch.nn import Parameter
  from allennlp.modules.attention.attention import Attention
  from allennlp.nn import util
  from allennlp.nn.activations import Activation
label_desc: Use image node_id 2 for calling the LinearAttention obj's underlying member method code with example usage: obj.reset_parameters() without return types